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CIFAR-10 [1]}: This dataset consists of \(60,000\)  (32 x 32) images belonging to 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck), and is divided into \(50,000\)  images to be used for training and \(10,000\)  for testing.

CIFAR-100 [1]}: Similar to CIFAR-10 dataset, CIFAR-100 consists of \(50,000\)  training images and \(10,000\)  testing images. There are 100 classes in this dataset, grouped into 20 super-classes. Each image comes with a “fine label" which is the class label and a “coarse label" which is the super-class to which it belongs. In our study, we make use of the fine labels.

SVHN [3]}: This real-world dataset comprises house numbers obtained from Google Street View images and consists of \(73,257\)  samples for training, \(26,032\)  images for testing and \(531,131\)  additional, less difficult samples which can be used as extra data for training. In our study, the training
portion of the dataset corresponding to isolated digits (10 classes) is used.

PlantVillage [4]} : This crowd-sourced dataset consists of \(54,309\)  images with 39 diseases of different crop plants. Three different versions of this dataset are provided: original RGB images with varied sizes, gray-scaled version of the raw images, and RGB images with just the leaf segmented and color corrected. In this work, we used the original RGB images.

